Evidence map›Paper›PMID 41404314›Full record

ArticleEFSA journal. European Food Safety Authority2025

Probabilistic MIC modelling for AMR risk assessment.

Alba Puente Baños, Constantine-Richard Stefanou, Leonardos Stathas, Styliani-Dimitra Papagianeli, Avelino Álvarez Ordóñez, Agapi Doulgeraki, Konstantinos Koutsoumanis

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In one paragraph

Article in EFSA journal. European Food Safety Authority, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Probabilistic MIC modelling for AMR risk assessment.EFSA journal. European Food Safety Authority · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alba Puente BañosDepartment of Food Hygiene and Technology Universidad de León León Spain.
Constantine-Richard StefanouLaboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.
Leonardos StathasLaboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.
Styliani-Dimitra PapagianeliLaboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.
Avelino Álvarez OrdóñezDepartment of Food Hygiene and Technology Universidad de León León Spain.
Agapi DoulgerakiLaboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.
Konstantinos KoutsoumanisLaboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the minimum inhibitory concentration (MIC) of antibiotics is crucial for developing targeted strategies to combat antimicrobial resistance (AMR) in the food chain. Traditional MIC testing methods provide a single and deterministic value without taking into account the inherent inter-individual variability in the response of foodborne pathogens to antimicrobials. A more accurate representation of the complex interactions influencing AMR in foodborne pathogens could be probabilistic MIC modelling. The incorporation of probabilistic MIC modelling into a classic quantitative microbiological risk assessment (QMRA) of AMR for food pathogens could provide a more realistic risk estimate. The EU-FORA fellowship's objective was to develop a probabilistic QMRA for an antimicrobial-resistant foodborne pathogen in a food product, incorporating the variability of the MIC of individual bacterial cells. The project involved the collection of data on

Indexed as

AMRcooked hamListeria monocytogenesQMRARTEscMIC

Identifiers

PMID41404314
PMCPMC12703809

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.